To stay competitive, we must optimize machining operations to produce higher-quality products and increase productivity. Determining the ideal settings to attain the lowest surface roughness and the maximum rate of material removal—two of the primary quality responses—is the goal of the majority of these machining techniques. The main topic of this essay is the optimisation of the parameters for the procedure in the dry turning of SS304 PVD-coated carbide inserts. The methodology used for this process optimisation is called the Desirability Optimisation Methodology (DOM), which is multi-objective and combines the desirability function approach and single-objective optimisation Response Surface Methodology (RSM) in conjunction with the Taguchi approach. Cutting specifics (depth of cut, feed rate, and speed) and dependent output variables (material removal rate (MRR) and the profile's arithmetic mean deviation surface roughness (Ra)) are investigated using the orthogonal-array design L9 by Taguchi (3 × 3) and analysis of variance using ANOVA. In order to anticipate the Ra and MRR models, an equation that stems from the first-order model was developed using analysis of regression. The linear prediction model of the first order was created using multiple regression analysis to determine the independent variables and surface roughness/MRR association. The findings of the trials show that the most crucial factors are the cut depth and feed rate, which are significant elements impacting MRR and Ra, respectively, and that the mathematical models that were created properly depict the response index in the range of parameters evaluated. Ultimately, confirmatory experiments have shown that changing settings to maximise MRR and minimise surface roughness could be optimally achieved using the desirability function technique, linear regression models, and Taguchi's method.